R is the platform of choice for a considerable segment of statisticians and data scientists. The popularity of R is understandable: it is quite easy to use, it is free, and it includes a host of useful packages. In recent years, some alternatives have emerged as competitors for users in, and around, this segment. Amongst these, Python is perhaps the most well known. Although it has received less attention, the Julia language holds significant promise for use in statistics and data science. Some of the advantages of Julia will be discussed and compared directly with R. The ease with which one could switch from Julia to R -- or learn Julia coming from an R background -- will also be considered.